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models.py
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models.py
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import torch.nn as nn
import torchvision
class SimpleCNNModel(nn.Module):
name = "SimpleCNNModel"
@staticmethod
def make_cnn_block(in_channels, out_channels, kernel_size=(3, 3), add_batchnorm=False):
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2))
)
if add_batchnorm:
block.add_module(name='batch_norm', module=nn.BatchNorm2d(out_channels))
return block
def __init__(self, num_classes: int):
super().__init__()
self.cnn_block_1 = self.make_cnn_block(1, 16, (3, 3), add_batchnorm=True)
self.dropout1 = nn.Dropout(0.1)
self.cnn_block_2 = self.make_cnn_block(16, 32, (3, 3), add_batchnorm=True)
self.dropout2 = nn.Dropout(0.1)
self.cnn_block_3 = self.make_cnn_block(32, 64, (3, 3), add_batchnorm=True)
self.dropout3 = nn.Dropout(0.1)
self.fc1 = nn.Sequential(
nn.Linear(1024, 512),
nn.ReLU(inplace=True)
)
self.dropout4 = nn.Dropout(0.1)
self.fc2 = nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(inplace=True)
)
self.fc3 = nn.Sequential(
nn.Linear(256, 128),
nn.ReLU(inplace=True)
)
self.fc4 = nn.Sequential(
nn.Linear(128, num_classes)
)
def forward(self, **kwargs):
op = self.cnn_block_1(kwargs['x'])
op = self.dropout1(op)
op = self.cnn_block_2(op)
op = self.dropout1(op)
op = self.cnn_block_3(op)
op = self.dropout1(op)
op = op.flatten(1)
op = self.fc1(op)
op = self.dropout1(op)
op = self.fc2(op)
op = self.fc3(op)
op = self.fc4(op)
return op
class SimpleCNNModel2(nn.Module):
name = "SimpleCNNModel2"
@staticmethod
def make_cnn_block(in_channels, out_channels, kernel_size=(3, 3), activate=True, pooling=False,
add_batchnorm=False):
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size)
)
if activate:
block.add_module(name='activation', module=nn.ReLU(inplace=True))
if pooling:
block.add_module(name='pooling', module=nn.MaxPool2d(kernel_size=(2, 2)))
if add_batchnorm:
block.add_module(name='batch_norm', module=nn.BatchNorm2d(out_channels))
return block
def __init__(self, num_classes: int):
super().__init__()
self.cnn_block_1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=(3, 3), padding=(2, 2)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(32)
) # 52
self.cnn_block_2 = nn.Sequential(
nn.Conv2d(32, 32, kernel_size=(3, 3)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(32)
) # 50
self.maxpool_1 = nn.MaxPool2d((2, 2)) # 25
self.cnn_block_3 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=(3, 3)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(64)
) # 23
self.cnn_block_4 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=(3, 3)),
nn.ReLU(inplace=True),
nn.BatchNorm2d(64)
) # 21
self.maxpool_2 = nn.MaxPool2d((2, 2)) # 10
self.fc_block_1 = nn.Sequential(
nn.Linear(6400, 3200),
nn.ReLU(inplace=True),
nn.BatchNorm1d(3200)
)
self.fc_block_2 = nn.Sequential(
nn.Linear(3200, 1024),
nn.ReLU(inplace=True),
nn.BatchNorm1d(1024)
)
self.fc_block_3 = nn.Sequential(
nn.Linear(1024, 512),
nn.ReLU(inplace=True),
nn.BatchNorm1d(512)
)
self.final_layer = nn.Linear(512, num_classes)
def forward(self, **kwargs):
op = self.cnn_block_1(kwargs['x'])
op = self.cnn_block_2(op)
op = self.maxpool_1(op)
op = self.cnn_block_3(op)
op = self.cnn_block_4(op)
op = self.maxpool_2(op)
op = op.flatten(1)
op = self.fc_block_1(op)
op = self.fc_block_2(op)
op = self.fc_block_3(op)
op = self.final_layer(op)
return op
class AlexNetTransfer(nn.Module):
name = "ALexNetTransfer"
@staticmethod
def make_cnn_block(in_channels, out_channels, activate=True, pooling=False, add_batchnorm=False, **kwargs):
cnn_args = kwargs.get('cnn_args', {})
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=cnn_args.get('kernel_size', (3, 3)),
stride=cnn_args.get('stride', (1, 1)), padding=cnn_args.get('padding', (104, 104))
)
)
if activate:
block.add_module(name='activation', module=nn.ReLU(inplace=True))
if pooling:
pooling_args = kwargs.get('pooling_args', {})
block.add_module(name='pooling', module=nn.MaxPool2d(kernel_size=pooling_args.get('kernel_size', (2, 2)),
stride=pooling_args.get('stride', (1, 1))))
if add_batchnorm:
block.add_module(name='batch_norm', module=nn.BatchNorm2d(out_channels))
return block
def __init__(self, num_classes: int):
super().__init__()
self.alex_net = torchvision.models.alexnet(weights=torchvision.models.AlexNet_Weights.DEFAULT)
# first_cnn = model.get_submodule('features').get_submodule('0')
# first_cnn.parameters(in_channels=1)
clf_module = self.alex_net.get_submodule('classifier')
clf_module.add_module('7', nn.ReLU(inplace=True))
# clf_module.add_module('final_layer', nn.Linear(in_features=1000, out_features=num_classes))
self.final_layer = nn.Linear(1000, num_classes)
def forward(self, **kwargs):
op = self.alex_net(kwargs['x'])
op = self.final_layer(op)
return op
class MobileNetV2(nn.Module):
name = "MobileNetV2"
@staticmethod
def make_cnn_block(in_channels, out_channels, activate=True, pooling=False, add_batchnorm=False, **kwargs):
cnn_args = kwargs.get('cnn_args', {})
block = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=cnn_args.get('kernel_size', (3, 3)),
stride=cnn_args.get('stride', (1, 1)), padding=cnn_args.get('padding', (104, 104))
)
)
if activate:
block.add_module(name='activation', module=nn.ReLU(inplace=True))
if pooling:
pooling_args = kwargs.get('pooling_args', {})
block.add_module(name='pooling', module=nn.MaxPool2d(kernel_size=pooling_args.get('kernel_size', (2, 2)),
stride=pooling_args.get('stride', (1, 1))))
if add_batchnorm:
block.add_module(name='batch_norm', module=nn.BatchNorm2d(out_channels))
return block
def __init__(self, num_classes: int):
super().__init__()
self.mobilenet = torchvision.models.mobilenet_v2(weights=torchvision.models.MobileNet_V2_Weights.DEFAULT)
mobilenet_clf = self.mobilenet.get_submodule('classifier')
mobilenet_clf.add_module('2', nn.ReLU(inplace=True))
self.final_layer = nn.Linear(1000, num_classes)
def forward(self, **kwargs):
op = self.mobilenet(kwargs['x'])
op = self.final_layer(op)
return op